After using our baseline values of \(B_m\)  and \(P_m\)  to output the number of stripes and interstripes in Step 3, we turn to the hyper-parameters \(\tau ^\text{min}_m\)  and \(\alpha _m\)  that affect our measurements of curviness. We evaluate 100 CA and ABM patterns, excluding those with broken (inter)stripes, under minimum \(X^\text{d}\)  cluster size \(\tau ^\text{min}_m=20,30,...,90,100\)  cells and \(\alpha \)  radius \(\alpha _m=50,60,..,190,200\)  \(\mu \) m. Because McGuirl et al. [1]} clustered cells into interstripes for ABM [2]} patterns without any need to remove non-interstripe clusters first, we note that \(\tau ^\text{min}_\text{ABM}=0\)  cells is valid. For CA patterns, as we show in Figures REF (a)–(b), boundary roughness makes it challenging to visually specify which \(X^\text{d}\)  cells should be removed before computing curviness. Sweeping across values of \(\tau ^\text{min}_\text{CA}\)  and \(\alpha _\text{CA}\) , all values of the minimum cluster size \(\tau ^\text{min}_m\)  between 20 and 100 cells lead to the same curviness results, so we do not show this heatmap. In contrast, choosing \(\tau ^\text{min}_\text{CA}\)  much lower than 20 cells does not reliably group \(X^\text{d}\)  cells into \(N_\text{cl}\)  clusters that are interstripes, and it causes errors in our algorithm: when \(\tau ^\text{min}_\text{CA}\)  is too low, outlier cells in CA patterns are sometimes selected as one of the interstripe clusters in Step 4. We thus choose \(\tau ^\text{min}_\text{CA}= 60\)  cells as our baseline value, and set \(\tau ^\text{min}_\text{ABM}=\tau ^\text{min}_\text{CA}\)  for consistency.
